Examples with RegressionTreeModel org.knime.base.node.mine.treeensemble.model.RegressionTreeModel used on opensource projects

Example 1 with RegressionTreeModel

use of org.knime.base.node.mine.treeensemble.model.RegressionTreeModel in project knime-core by knime.

the class RegressionTreePredictorCellFactory method getCells.

/**
 * {@inheritDoc}
 */
@Override
public DataCell[] getCells(final DataRow row) {
    RegressionTreeModelPortObject modelObject = m_predictor.getModelObject();
    final RegressionTreeModel treeModel = modelObject.getModel();
    int size = 1;
    DataCell[] result = new DataCell[size];
    DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
    PredictorRecord record = treeModel.createPredictorRecord(filterRow, m_learnSpec);
    if (record == null) {
        // missing value
        Arrays.fill(result, DataType.getMissingCell());
        return result;
    }
    TreeModelRegression tree = treeModel.getTreeModel();
    TreeNodeRegression match = tree.findMatchingNode(record);
    double nodeMean = match.getMean();
    result[0] = new DoubleCell(nodeMean);
    return result;
}

Example 2 with RegressionTreeModel

use of org.knime.base.node.mine.treeensemble.model.RegressionTreeModel in project knime-core by knime.

the class RegressionTreeLearnerNodeModel method execute.

// /**
// * @param ensembleSpec
// * @param ensembleModel
// * @param inSpec
// * @return
// * @throws InvalidSettingsException
// */
// private TreeEnsemblePredictor createOutOfBagPredictor(final TreeEnsembleModelPortObjectSpec ensembleSpec,
// final TreeEnsembleModelPortObject ensembleModel, final DataTableSpec inSpec) throws InvalidSettingsException {
// TreeEnsemblePredictorConfiguration ooBConfig = new TreeEnsemblePredictorConfiguration(true);
// String targetColumn = m_configuration.getTargetColumn();
// String append = targetColumn + " (Out-of-bag)";
// ooBConfig.setPredictionColumnName(append);
// ooBConfig.setAppendPredictionConfidence(true);
// ooBConfig.setAppendClassConfidences(true);
// ooBConfig.setAppendModelCount(true);
// return new TreeEnsemblePredictor(ensembleSpec, ensembleModel, inSpec, ooBConfig);
// }
/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inObjects[0];
    DataTableSpec spec = t.getDataTableSpec();
    final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(spec);
    String warn = learnRearranger.getWarning();
    BufferedDataTable learnTable = exec.createColumnRearrangeTable(t, learnRearranger, exec.createSubProgress(0.0));
    DataTableSpec learnSpec = learnTable.getDataTableSpec();
    TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
    ExecutionMonitor readInExec = exec.createSubProgress(0.1);
    ExecutionMonitor learnExec = exec.createSubProgress(0.9);
    TreeDataCreator dataCreator = new TreeDataCreator(m_configuration, learnSpec, learnTable.getRowCount());
    exec.setProgress("Reading data into memory");
    TreeData data = dataCreator.readData(learnTable, m_configuration, readInExec);
    m_hiliteRowSample = dataCreator.getDataRowsForHilite();
    m_viewMessage = dataCreator.getViewMessage();
    String dataCreationWarning = dataCreator.getAndClearWarningMessage();
    if (dataCreationWarning != null) {
        if (warn == null) {
            warn = dataCreationWarning;
        } else {
            warn = warn + "\n" + dataCreationWarning;
        }
    }
    readInExec.setProgress(1.0);
    exec.setMessage("Learning tree");
    // TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
    // TreeEnsembleModel model;
    // try {
    // model = learner.learnEnsemble(learnExec);
    // } catch (ExecutionException e) {
    // Throwable cause = e.getCause();
    // if (cause instanceof Exception) {
    // throw (Exception)cause;
    // }
    // throw e;
    // }
    RandomData rd = m_configuration.createRandomData();
    TreeLearnerRegression treeLearner = new TreeLearnerRegression(m_configuration, data, rd);
    TreeModelRegression regTree = treeLearner.learnSingleTree(learnExec, rd);
    RegressionTreeModel model = new RegressionTreeModel(m_configuration, data.getMetaData(), regTree, data.getTreeType());
    RegressionTreeModelPortObjectSpec treePortObjectSpec = new RegressionTreeModelPortObjectSpec(learnSpec);
    RegressionTreeModelPortObject treePortObject = new RegressionTreeModelPortObject(model, treePortObjectSpec);
    learnExec.setProgress(1.0);
    m_treeModelPortObject = treePortObject;
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { treePortObject };
}